2 citations · 2 across the 4 of their papers we have counts for
4 papers
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Kaixiong Gong, Kaituo Feng, Bohao Li +8
Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While th…
Analyzing The Language of Visual Tokens
David M. Chan, Rodolfo Corona, Joonyong Park +3
With the introduction of transformer-based models for vision and language tasks, such as LLaVA and Chameleon, there has been renewed interest in the discrete tokenized representati…
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning
Dantong Niu, Yuvan Sharma, Giscard Biamby +5
In recent years, instruction-tuned Large Multimodal Models (LMMs) have been successful at several tasks, including image captioning and visual question answering; yet leveraging th…
Masked Autoencoders Enable Efficient Knowledge Distillers
Yutong Bai, Zeyu Wang, Junfei Xiao +5
This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel recons…